Probabilistic microsimulation to examine the cost-effectiveness of hospital admission screening strategies for carbapenemase-producing enterobacteriaceae (CPE) in the United Kingdom

Background Antimicrobial resistance has been recognised as a global threat with carbapenemase- producing-Enterobacteriaceae (CPE) as a prime example. CPE has similarities to COVID-19 where asymptomatic patients may be colonised representing a source for onward transmission. There are limited treatment options for CPE infection leading to poor outcomes and increased costs. Admission screening can prevent cross-transmission by pre-emptively isolating colonised patients. Objective We assess the relative cost-effectiveness of screening programmes compared with no- screening. Methods A microsimulation parameterised with NHS Scotland date was used to model scenarios of the prevalence of CPE colonised patients on admission. Screening strategies were (a) two-step screening involving a clinical risk assessment (CRA) checklist followed by microbiological testing of high-risk patients; and (b) universal screening. Strategies were considered with either culture or polymerase chain reaction (PCR) tests. All costs were reported in 2019 UK pounds with a healthcare system perspective. Results In the low prevalence scenario, no screening had the highest probability of cost-effectiveness. Among screening strategies, the two CRA screening options were the most likely to be cost-effective. Screening was more likely to be cost-effective than no screening in the prevalence of 1 CPE colonised in 500 admitted patients or more. There was substantial uncertainty with the probabilities rarely exceeding 40% and similar results between strategies. Screening reduced non-isolated bed-days and CPE colonisation. The cost of screening was low in relation to total costs. Conclusion The specificity of the CRA checklist was the parameter with the highest impact on the cost-effectiveness. Further primary data collection is needed to build models with less uncertainty in the parameters. Supplementary Information The online version contains supplementary material available at 10.1007/s10198-021-01419-5.


Model Parameters
, [4], [5] Model is run separately for 4 levels of this parameter.

Distribution Source Notes
anything that is expressed in yearly terms startAge Age at start Table 4 ISD Patient Level simulation. Each patient created is allocated an age from a distribution described in Table 4. Time_Horizon Cycles of simulation 1,096 The simulation runs for 1,096 days.

Infectious disease modelling
The model allows modelling of spread of CPE in simulated hospital. The probability of becoming CPE colonised depends on the number of other colonised patients in the wards. Isolated patients are not included in this calculation and these patients have a stable colonisation status but can develop infection if CPE true positive. Discharged true colonised patients cannot transmit CPE or develop infection in the community but have a higher risk of re-admission.
In hospital transmission occurs at rate β which is the basic reproductive rate of CPE but transmission depends on the number of colonised patients in the hospital in the following manner: βI/N, with I representing the number of colonised patients in the wards, and N the total number of patients in the wards. Therefore, the probability for a susceptible patient to not acquire CPE at a given day is e -βΙ/Ν and the probability to acquire colonisation is 1-e -βΙ/Ν . The model assumes a fixed basic reproductive rate for all individuals and that patients mix homogeneously in the hospital [5,16]. It is assumed that CPE colonisation is permanent and a colonised patient remains colonised until death.

Patients who develop CPE local infection remain in isolation but patients with CPE
Systemic infection are moved to intensive care unit (ICU). Patients who survive infection are discharged directly into the community and they do not return back to the wards which may not be what happens in reality but since it was applied similarly across all strategies had no impact on model results. Daily probability of death due to CPE infection was calculated from weekly, monthly or yearly mortality rates reported in the literature  Deterministic analyses based on single model runs and point estimates of parameters were used to select parameters for sensitivity analysis (not shown). In sensitivity analysis we investigate the impact of selected parameters on the cost-effectiveness results with tornado diagrams. The deterministic analyses are not presented due to the nature of the modelled probabilistic processes, e.g. death or discharge, and due to the considerable parameter uncertainty. The usefulness of the tornado diagram lies in that it gives an indication of the relative importance of parameters to the costeffectiveness results.

Additional Results
To investigate the impact of specific parameters on our results we constructed two tornado diagrams of incremental net monetary benefits (INMB) between: strategy 1 (no screening) with strategy 2 (CRA with culture) and strategy 1 with strategy 3 (CRA with PCR). Tornado diagrams can only show a comparison between two strategies and we chose these to show a comparison between the current NHS policy (with culture and PCR) and the do nothing option. Tornado diagrams study the impact of any number of individual parameters on the cost-effectiveness results, then present them together in a single analysis. An INMB tornado diagram reports the range of INMBs generated for each parameter's uncertainty range. The parameters included in the diagram are the following: Prevalence on admission, specificity of CRA, CRA take up rate, screening uptake rate for CRA positive patients, sensitivity of CRA and screening programme uptake rate. On the graph the blue portion of the bar represents the INMB range when the parameter value is lower than its base case value. The red portion of the bar represents the INMB range when the parameter is higher than its base case value. The variable range is given in parentheses next to each bar on the graph. A third tornado diagram is also presented which shows the impact of these variables on effectiveness (QALYs) across all strategies. The tornado diagrams show the CPE colonised prevalence on admission has a 63% impact on the results followed by CRA specificity and the CRA take up rate, which have an impact of 25% and 8%, respectively. The screening uptake in the CRA strategies and the sensitivity of CRA have 3% and 2% impact on the results, respectively. Given that prevalence on admission was kept constant in the costeffectiveness scenarios presented in the main text the parameter with the biggest impact on effectiveness and therefore cost-effectiveness was the specificity of CRA as shown in Figure 6. The results in the scenario of prevalence of 1 in 1,000 patients CPE colonised on admission are shown in Figure 7. In this scenario Strategy 1 "no screening" was the most frequently optimal strategy followed by Strategy 2 (CRA with culture) and Strategy 3 (CRA with PCR). In this scenario these three strategies were very close in terms of the probability of cost-effectiveness. No screening was shown to be the most likely to be cost-effective but the difference with the CRA screening strategies was only a few percentage points. In fact Strategy 3 (CRA screening with PCR) becomes the most likely to be cost-effective when willingness-to pay exceeds £40,000 per QALY gained. The universal screening strategies were not likely to be cost-effective at this level of prevalence exceeding 10% probability over the top of the NHS range of willingness-to-pay per QALY. The cost-effectiveness acceptability curve in the scenario of 1 in 500 patients being CPE colonised on admission is shown in Figure 8. At this level of prevalence targeted screening becomes cost-saving in relation to no screening. The optimal strategy across the NHS willingness-to-pay range was Strategy 2 (CRA with culture) followed by Strategy 1 (no screening) and Strategy 3 (CRA with PCR). Universal screening was unlikely to be cost-effective in this scenario. Figure 9 Cost-effectiveness acceptability curve at CPE colonised prevalence on admission 1 in 100 patients. "Chr agar" on figure refers to culture microbiological test. "PCR all" and "chr agar all" refers to universal screening strategies.
The cost-effectiveness acceptability curve in the scenario of 1 in 100 patients being CPE colonised on admission is shown in Figure 9. At this high prevalence CRA screening strategies are by the far the most cost-effective options. Throughout the NHS willingness-to-pay range Strategy 3 (CRA with PCR) was the most cost-effective option very closely followed by Strategy 2 (CRA with culture). This result indicates that the extra speed PCR gives in microbiological test results improves outcomes at this high level of prevalence. It is interesting to note that Strategy 4 (universal screening with culture) had more than 20% probability of cost-effectiveness in the NHS range and approached 30% at higher levels of willingness-to-pay per QALY gained. As CPE positive prevalence on admission increases better coverage given from universal screening became more important to achieve cost-effectiveness. No screening and universal screening with PCR were unlikely to be cost-effective in this scenario.